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检索条件"主题词=deep learning in robotics and automation"
221 条 记 录,以下是61-70 订阅
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CNN Based Road User Detection Using the 3D Radar Cube
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 1263-1270页
作者: Palffy, Andras Dong, Jiaao Kooij, Julian F. P. Gavrila, Dariu M. Delft Univ Technol Intelligent Vehicles Grp NL-2628 Delft Netherlands
This letter presents a novel radar based, single-frame, multi-class detection method for moving road users (pedestrian, cyclist, car), which utilizes low-level radar cube data. The method provides class information bo... 详细信息
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Reinforcement learning for POMDP: Partitioned Rollout and Policy Iteration With Application to Autonomous Sequential Repair Problems
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IEEE robotics AND automation LETTERS 2020年 第3期5卷 3967-3974页
作者: Bhattacharya, Sushmita Badyal, Sahil Wheeler, Thomas Gil, Stephanie Bertsekas, Dimitri Arizona State Univ REACT Lab Tempe AZ 85287 USA Arizona State Univ Comp Sci 699 S Mill Ave Tempe AZ 85287 USA MIT Engn 77 Mass Ave Cambridge MA 02139 USA Arizona State Univ Computat Decis Making 699 S Mill Ave Tempe AZ 85287 USA
In this letter we consider infinite horizon discounted dynamic programming problems with finite state and control spaces, and partial state observations. We discuss an algorithm that uses multistep lookahead, truncate... 详细信息
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Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 3050-3057页
作者: Nubert, Julian Koehler, Johannes Berenz, Vincent Allgoewer, Frank Trimpe, Sebastian Max Planck Inst Intelligent Syst Intelligent Control Syst Grp D-70569 Stuttgart Germany Swiss Fed Inst Technol CH-8092 Zurich Switzerland Univ Stuttgart Inst Syst Theory & Automat Control D-70550 Stuttgart Germany Max Planck Inst Intelligent Syst Autonomous Mot Dept D-72076 Tubingen Germany
Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) n... 详细信息
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Self-Supervised Linear Motion Deblurring
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 2475-2482页
作者: Liu, Peidong Janai, Joel Pollefeys, Marc Sattler, Torsten Geiger, Andreas Swiss Fed Inst Technol Comp Vis & Geometry Grp Dept Comp Sci CH-8092 Zurich Switzerland Max Planck Inst Intelligent Syst Autonomous Vis Grp D-41296 Tubingen Germany Microsoft Mixed Real & Artificial Intelligence La CH-8001 Zurich Switzerland Chalmers Univ Technol Comp Vis & Med Image Anal Grp S-41296 Gothenburg Sweden
Motion blurry images challenge many computer vision algorithms, e.g., feature detection, motion estimation, or object recognition. deep convolutional neural networks are state-of-the-art for image deblurring. However,... 详细信息
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RSL-Net: Localising in Satellite Images From a Radar on the Ground
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 1087-1094页
作者: Tang, Tim Yuqing De Martini, Daniele Barnes, Dan Newman, Paul Univ Oxford Oxford Robot Inst Oxford OX1 2JD England
This letter is about localising a vehicle in an overhead image using FMCW radar mounted on a ground vehicle. FMCW radar offers extraordinary promise and efficacy for vehicle localisation. It is impervious to all weath... 详细信息
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Cross-Domain Motion Transfer via Safety-Aware Shared Latent Space Modeling
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 2634-2641页
作者: Choi, Sungjoon Kim, Joohyung Disney Res Glendale CA 91201 USA Univ Illinois Champaign IL 61820 USA
This letter presents a data-driven motion retargeting method with safety considerations. In particular, we focus on handling self-collisions while transferring poses between different domains. To this end, we first pr... 详细信息
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iART: learning From Demonstration for Assisted Robotic Therapy Using LSTM
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 477-484页
作者: Pareek, Shrey Kesavadas, Thenkurussi Univ Illinois Dept Ind & Enterprise Syst Engn Champaign IL 61820 USA
In this letter, we present an intelligent Assistant for Robotic Therapy (iART), that provides robotic assistance during 3D trajectory tracking tasks. We propose a novel LSTM-based robot learning from demonstration (Lf... 详细信息
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OriNet: Robust 3-D Orientation Estimation With a Single Particular IMU
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 399-406页
作者: Esfahani, Mahdi Abolfazli Wang, Han Wu, Keyu Yuan, Shenghai Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore
Estimating the robot's heading is a crucial requirement in odometry systems which are attempting to estimate the movement trajectory of a robot. Small errors in the orientation estimation result in a significant d... 详细信息
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A General Framework to Increase Safety of learning Algorithms for Dynamical Systems Based on Region of Attraction Estimation
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IEEE TRANSACTIONS ON robotics 2020年 第5期36卷 1472-1490页
作者: Zhou, Zhehua Oguz, Ozgur S. Leibold, Marion Buss, Martin Tech Univ Munich Chair Automat Control Engn D-80290 Munich Germany
Although the state-of-the-art learning approaches exhibit impressive results for dynamical systems, only a few applications on real physical systems have been presented. One major impediment is that the intermediate p... 详细信息
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RLBench: The Robot learning Benchmark & learning Environment
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 3019-3026页
作者: James, Stephen Ma, Zicong Arrojo, David Rovick Davison, Andrew J. Imperial Coll London Dyson Robot Lab London SW7 2BU England Imperial Coll London UROP London SW7 2BU England
We present a challenging new benchmark and learning-environment for robot learning: RLBench. The benchmark features 100 completely unique, hand-designed tasks, ranging in difficulty from simple target reaching and doo... 详细信息
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